# pipeline

VLM model → feature extraction → clustering → data filtering → fine-tuning → LoRA export → LLVM serve → inference → evaluation

# 001 VLM model → feature extraction
```bash
cd extract

run clustering_example_qwen notebook
```

# 002 clustering grid search
```bash
cd cluster

# dbscan
python auto_cluster.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json --method dbscan

# gmm
python gmm_extensive.py --embeddings_path /home/nguyendc/sonnh/embedding-clustering/extract/embeddings_factures_osteopathie_1k_qwen.json
```

- Cluster Result will save at: 
    - cluster/dbscan_results.json
    - cluster/gmm_final_results.json


# 003 filter data

```bash
cd filter
bash run_filter.sh
```

- EMBEDDINGS_PATH: path to the embedding file generated in step 001
- CLUSTERING_RESULTS_PATH: path to the file containing the clustering information generated in step 002
- OUTPUT_PATH: path to save the retained files

- selection_ratio: proportion of data to be passed into the filter 
- center_ratio: proportion of center points to take 
- border_ratio: proportion of boundary points to take


# 004 create data from full data and filter data

```bash
cd filter

python3 create_label_data.py
```

- dbscan_results_path: OUTPUT_PATH produced in step 003
- label_data_path: ground truth after being filtered from the filtered data, has the same format as the full dataset, used for finetuning the VLM model


# visual and check data

```bash
cd check_filter
bash run.sh
```